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Nonlinear Projected Sparse Optimization Approach Based on Adam Algorithm for Microwave Imaging
A microwave imaging algorithm based on contrast-field equations is developed for sparse domains. The proposed algorithm is inspired by machine learning optimization schemes. More specifically it is based on Adam approach which is a first-order gradient optimization algorithm that has been studied intensively in optimizing artificial neural networks. To enforce sparsity constraint, the permittivity contrast at each iteration is subjected to a projection operator. The proposed algorithm has faster convergence than another state of art steepest descent approach used for microwave Imaging.
Nonlinear Projected Sparse Optimization Approach Based on Adam Algorithm for Microwave Imaging
A microwave imaging algorithm based on contrast-field equations is developed for sparse domains. The proposed algorithm is inspired by machine learning optimization schemes. More specifically it is based on Adam approach which is a first-order gradient optimization algorithm that has been studied intensively in optimizing artificial neural networks. To enforce sparsity constraint, the permittivity contrast at each iteration is subjected to a projection operator. The proposed algorithm has faster convergence than another state of art steepest descent approach used for microwave Imaging.
Nonlinear Projected Sparse Optimization Approach Based on Adam Algorithm for Microwave Imaging
Desmal, Abdulla (author) / Sandhu, Ali Imran (author) / Bagci, Hakan (author)
2020-02-01
191830 byte
Conference paper
Electronic Resource
English
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